Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

Abstract

It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.